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Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
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Power and Energy01:12

Power and Energy

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The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
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Power System Distribution01:25

Power System Distribution

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Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
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Energy and Power Signals01:17

Energy and Power Signals

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Related Experiment Video

Updated: Jun 20, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

A unified multi-agent optimization framework for intelligent PV-integrated smart energy systems.

Muhammad Muneeb Khan1, Sadiq Ahmad1, Muhammad Naeem1

  • 1Department of Electrical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan.

Scientific Reports
|April 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a smart energy management framework for communities with solar panels (PV). The multi-agent reinforcement learning approach optimizes energy use, reducing costs by up to 69% while maintaining comfort and minimizing equipment wear.

Keywords:
Battery degradationDemand responseEnergy community/microgridMulti-agent reinforcement learning (MARL)Peer-to-peer (P2P) energy trading

Related Experiment Videos

Last Updated: Jun 20, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy Management
  • Smart Grids and Community Energy

Background:

  • Increasing integration of photovoltaic (PV) generation and distributed energy resources in residential areas necessitates advanced energy management.
  • Conventional cost-based strategies are insufficient for scalable, intelligent management of complex community energy systems.

Purpose of the Study:

  • To propose a unified multi-agent optimization framework for photovoltaic-integrated smart energy communities.
  • To incorporate design-aware PV modeling, degradation-aware energy storage control, indoor comfort preservation, and peer-to-peer (P2P) energy exchange.

Main Methods:

  • Formulating the community energy management as a multi-agent Markov game with autonomous household agents.
  • Developing a multi-agent reinforcement learning (MARL) approach using a centralized training and decentralized execution (CTDE) paradigm.
  • Addressing nonlinear and multi-objective optimization challenges inherent in community energy management.

Main Results:

  • The CTDE-based MARL framework significantly enhances operational efficiency and scalability.
  • Unified operational costs are reduced by up to 69% compared to greedy control and over 37% versus single-agent reinforcement learning.
  • The framework minimizes asset degradation, preserves indoor comfort, and approaches centralized optimal performance.

Conclusions:

  • The proposed MARL framework offers a scalable and efficient solution for managing PV-integrated smart energy communities.
  • It effectively balances operational cost reduction, asset longevity, and user comfort.
  • The framework demonstrates strong potential for practical implementation in intelligent residential energy systems.